Method and apparatus for point cloud coding

ABSTRACT

In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. The processing circuitry determines a flag that indicates an enable/disable control for saving coding state in a largest coding unit (LCU) based coding of a point cloud. In some examples, the processing circuitry stores coding state information before a coding of a first LCU; and in response to the flag indicating an enable control, the processing circuitry restores, a coding state according to the stored coding state information before a coding of a second LCU. In some examples, in response to the flag indicating an enable control, the processing circuitry stores the coding state information before the coding of the first LCU. In some examples, in response to the flag indicating a disable control, the processing circuitry skip the storing/restoring of the coding state information.

INCORPORATION BY REFERENCE

This present application claims the benefit of priority to U.S.Provisional Application No. 63/139,161, “Additional Information onNode-Based Geometry and Attribute Coding for a Point Cloud” filed onJan. 19, 2021, which is incorporated by reference herein in itsentirety.

TECHNICAL FIELD

The present disclosure describes embodiments generally related to pointcloud coding.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Various technologies are developed to capture and represent the world,such as objects in the world, environments in the world, and the like in3-dimensional (3D) space. 3D representations of the world can enablemore immersive forms of interaction and communication. Point clouds canbe used as a 3D representation of the world. A point cloud is a set ofpoints in a 3D space, each with associated attributes, e.g. color,material properties, texture information, intensity attributes,reflectivity attributes, motion related attributes, modality attributes,and various other attributes. Such point clouds may include largeamounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for pointcloud compression and decompression. In some examples, an apparatus forpoint cloud compression/decompression includes processing circuitry. Theprocessing circuitry determines a flag that indicates an enable/disablecontrol for saving coding state in a largest coding unit (LCU) basedcoding of a point cloud. In some examples, the processing circuitrystores coding state information before a coding of a first LCU; and inresponse to the flag indicating an enable control, restores, a codingstate according to the stored coding state information before a codingof a second LCU. In some examples, in response to the flag indicating anenable control, the processing circuitry stores the coding stateinformation before the coding of the first LCU. In some examples, inresponse to the flag indicating a disable control, the processingcircuitry skipped the storing and/or the restoring of the coding stateinformation.

In some embodiments, the processing circuitry decodes the flag from atleast one of a sequence parameter set in a sequence header, a geometryparameter set in a geometry header, and a slice header.

In an embodiment, the processing circuitry decodes the flag from ageometry parameter set in a geometry header. Then, the processingcircuitry stores, the coding state information before a geometry codingof the first LCU, and restores, the coding state according to the storedcoding state information before a geometry coding of the second LCU.

In another embodiment, the processing circuitry decodes the flag from asequence parameter set in a sequence header. Then, the processingcircuitry stores, the coding state information before a geometry andattribute coding of the first LCU, and restores, the coding stateaccording to the stored coding state information before a geometry andattribute coding of the second LCU.

In some embodiments, the processing circuitry starts the coding of thesecond LCU without a completion of the coding of the first LCU.

In some examples, the processing circuitry skips the restoring of thecoding state according to the stored coding state information before thecoding of the second LCU in response to the flag indicating a disablecontrol for saving coding state.

In some examples, the processing circuitry skips the storing of thecoding state information in response to the flag indicating the disablecontrol for saving coding state.

In some examples, the coding state information includes at least one ofcontext information and history geometry occupancy information.

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by acomputer for point cloud encoding/decoding cause the computer to performany one or a combination of the methods for point cloudencoding/decoding.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a schematic illustration of a simplified block diagram of acommunication system in accordance with an embodiment;

FIG. 2 is a schematic illustration of a simplified block diagram of astreaming system in accordance with an embodiment;

FIG. 3 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 4 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 5 is a schematic illustration of a simplified block diagram of avideo decoder in accordance with an embodiment;

FIG. 6 is a schematic illustration of a simplified block diagram of avideo encoder in accordance with an embodiment;

FIG. 7 shows a block diagram of an encoder for encoding point cloudframes, according to some embodiments;

FIG. 8 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments;

FIG. 9 shows a diagram illustrating a partition of a cube based on theoctree partition technique according to some embodiments of thedisclosure.

FIG. 10 shows an example of an octree partition and an octree structurecorresponding to the octree partition according to some embodiments ofthe disclosure.

FIG. 11 shows three quadtree partition examples.

FIG. 12 shows three binary tree partition examples.

FIG. 13 shows a diagram of an octree structure illustrating breadthfirst coding order.

FIG. 14 shows a diagram of an octree structure illustrating depth firstcoding order.

FIG. 15 shows a predictive tree example.

FIG. 16 shows a diagram of using a direct/forward transform architecturein the lifting based attribute coding at an encoder side.

FIG. 17 shows a diagram of using an inverse transform architecture inthe lifting based attribute coding at a decoder side.

FIG. 18 shows a diagram of a region adaptive hierarchical transform(RAHT) forward transform architecture and a diagram of a RAHT inversetransform architecture.

FIG. 19 shows a tree structure corresponding to a point cloud accordingto some examples of the disclosure.

FIG. 20 shows a flow chart outlining a coding process for codinggeometry information and/or attribute information in some examples.

FIG. 21 shows a flow chart outlining another coding process for codinggeometry information and/or attribute information in some examples.

FIGS. 22A-22B show syntax table examples according to some embodimentsof the disclosure.

FIG. 23 shows a flow chart outlining a coding process for codinggeometry information and/or attribute information according to someembodiments of the disclosure.

FIG. 24 shows a flow chart outlining another coding process for codinggeometry information and/or attribute information according to someembodiments of the disclosure.

FIG. 25 shows a flow chart outlining a process example in accordancewith some embodiments.

FIG. 26 is a schematic illustration of a computer system in accordancewith an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Aspects of the disclosure provide point cloud coding (PCC) techniques.PCC can be performed according to various schemes, such as ageometry-based scheme that is referred to as G-PCC, a video coding basedscheme that is referred to as V-PCC, and the like. According to someaspects of the disclosure, the G-PCC encodes the 3D geometry directlyand is a purely geometry-based approach without much to share with videocoding, and the V-PCC is heavily based on video coding. For example,V-PCC can map a point of the 3D cloud to a pixel of a 2D grid (animage). The V-PCC scheme can utilize generic video codecs for pointcloud compression. Moving picture experts group (MPEG) is working onG-PCC standard and V-PCC standard that respectively using the G-PCCscheme and the V-PCC scheme.

Point Clouds can be widely used in many applications. For example, pointclouds can be used in autonomous driving vehicles for object detectionand localization; point clouds can be used in geographic informationsystems (GIS) for mapping, and can be used in cultural heritage tovisualize and archive cultural heritage objects and collections, etc.

Hereinafter, a point cloud generally may refer to a set of points in a3D space, each with associated attributes, e.g. color, materialproperties, texture information, intensity attributes, reflectivityattributes, motion related attributes, modality attributes, and variousother attributes. Point clouds can be used to reconstruct an object or ascene as a composition of such points. The points can be captured usingmultiple cameras, depth sensors or Lidar in various setups and may bemade up of thousands up to billions of points in order to realisticallyrepresent reconstructed scenes or objects. A patch generally may referto a contiguous subset of the surface described by the point cloud. Inan example, a patch includes points with surface normal vectors thatdeviate from one another less than a threshold amount.

Compression technologies can reduce the amount of data required torepresent a point cloud for faster transmission or reduction of storage.As such, technologies are needed for lossy compression of point cloudsfor use in real-time communications and six Degrees of Freedom (6 DoF)virtual reality. In addition, technology is sought for lossless pointcloud compression in the context of dynamic mapping for autonomousdriving and cultural heritage applications, and the like.

According to an aspect of the disclosure, the main philosophy behindV-PCC is to leverage existing video codecs to compress the geometry,occupancy, and texture of a dynamic point cloud as three separate videosequences. The extra metadata needed to interpret the three videosequences are compressed separately. A small portion of the overallbitstream is the metadata, which could be encoded/decoded efficientlyusing software implementation. The bulk of the information is handled bythe video codec.

FIG. 1 illustrates a simplified block diagram of a communication system(100) according to an embodiment of the present disclosure. Thecommunication system (100) includes a plurality of terminal devices thatcan communicate with each other, via, for example, a network (150). Forexample, the communication system (100) includes a pair of terminaldevices (110) and (120) interconnected via the network (150). In theFIG. 1 example, the first pair of terminal devices (110) and (120) mayperform unidirectional transmission of point cloud data. For example,the terminal device (110) may compress a point cloud (e.g., pointsrepresenting a structure) that is captured by a sensor (105) connectedwith the terminal device (110). The compressed point cloud can betransmitted, for example in the form of a bitstream, to the otherterminal device (120) via the network (150). The terminal device (120)may receive the compressed point cloud from the network (150),decompress the bitstream to reconstruct the point cloud, and suitablydisplay the reconstructed point cloud. Unidirectional data transmissionmay be common in media serving applications and the like.

In the FIG. 1 example, the terminal devices (110) and (120) may beillustrated as servers, and personal computers, but the principles ofthe present disclosure may be not so limited. Embodiments of the presentdisclosure find application with laptop computers, tablet computers,smart phones, gaming terminals, media players, and/or dedicatedthree-dimensional (3D) equipment. The network (150) represents anynumber of networks that transmit compressed point cloud between theterminal devices (110) and (120). The network (150) can include forexample wireline (wired) and/or wireless communication networks. Thenetwork (150) may exchange data in circuit-switched and/orpacket-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networks,and/or the Internet. For the purposes of the present discussion, thearchitecture and topology of the network (150) may be immaterial to theoperation of the present disclosure unless explained herein below.

FIG. 2 illustrates a simplified block diagram of a streaming system(200) in accordance with an embodiment. The FIG. 2 example is anapplication for the disclosed subject matter for a point cloud. Thedisclosed subject matter can be equally applicable to other point cloudenabled applications, such as, 3D telepresence application, virtualreality application, and the like.

The streaming system (200) may include a capture subsystem (213). Thecapture subsystem (213) can include a point cloud source (201), forexample light detection and ranging (LIDAR) systems, 3D cameras, 3Dscanners, a graphics generation component that generates theuncompressed point cloud in software, and the like that generates forexample point clouds (202) that are uncompressed. In an example, thepoint clouds (202) include points that are captured by the 3D cameras.The point clouds (202), depicted as a bold line to emphasize a high datavolume when compared to compressed point clouds (204) (a bitstream ofcompressed point clouds). The compressed point clouds (204) can begenerated by an electronic device (220) that includes an encoder (203)coupled to the point cloud source (201). The encoder (203) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The compressed point clouds (204) (or bitstream of compressedpoint clouds (204)), depicted as a thin line to emphasize the lower datavolume when compared to the stream of point clouds (202), can be storedon a streaming server (205) for future use. One or more streaming clientsubsystems, such as client subsystems (206) and (208) in FIG. 2 canaccess the streaming server (205) to retrieve copies (207) and (209) ofthe compressed point cloud (204). A client subsystem (206) can include adecoder (210), for example, in an electronic device (230). The decoder(210) decodes the incoming copy (207) of the compressed point clouds andcreates an outgoing stream of reconstructed point clouds (211) that canbe rendered on a rendering device (212).

It is noted that the electronic devices (220) and (230) can includeother components (not shown). For example, the electronic device (220)can include a decoder (not shown) and the electronic device (230) caninclude an encoder (not shown) as well.

In some streaming systems, the compressed point clouds (204), (207), and(209) (e.g., bitstreams of compressed point clouds) can be compressedaccording to certain standards. In some examples, video coding standardsare used in the compression of point clouds. Examples of those standardsinclude, High Efficiency Video Coding (HEVC), Versatile Video Coding(VVC), and the like.

FIG. 3 shows a block diagram of a V-PCC encoder (300) for encoding pointcloud frames, according to some embodiments. In some embodiments, theV-PCC encoder (300) can be used in the communication system (100) andstreaming system (200). For example, the encoder (203) can be configuredand operate in a similar manner as the V-PCC encoder (300).

The V-PCC encoder (300) receives point cloud frames as uncompressedinputs and generates bitstream corresponding to compressed point cloudframes. In some embodiments, the V-PCC encoder (300) may receive thepoint cloud frames from a point cloud source, such as the point cloudsource (201) and the like.

In the FIG. 3 example, the V-PCC encoder (300) includes a patchgeneration module (306), a patch packing module (308), a geometry imagegeneration module (310), a texture image generation module (312), apatch info module (304), an occupancy map module (314), a smoothingmodule (336), image padding modules (316) and (318), a group dilationmodule (320), video compression modules (322), (323) and (332), anauxiliary patch info compression module (338), an entropy compressionmodule (334), and a multiplexer (324).

According to an aspect of the disclosure, the V-PCC encoder (300),converts 3D point cloud frames into an image-based representation alongwith some meta data (e.g., occupancy map and patch info) that is used toconvert the compressed point cloud back into a decompressed point cloud.In some examples, the V-PCC encoder (300) can convert 3D point cloudframes into geometry images, texture images and occupancy maps, and thenuse video coding techniques to encode the geometry images, textureimages and occupancy maps into a bitstream. Generally, a geometry imageis a 2D image with pixels filled with geometry values associated withpoints projected to the pixels, and a pixel filled with a geometry valuecan be referred to as a geometry sample. A texture image is a 2D imagewith pixels filled with texture values associated with points projectedto the pixels, and a pixel filled with a texture value can be referredto as a texture sample. An occupancy map is a 2D image with pixelsfilled with values that indicate occupied or unoccupied by patches.

The patch generation module (306) segments a point cloud into a set ofpatches (e.g., a patch is defined as a contiguous subset of the surfacedescribed by the point cloud), which may be overlapping or not, suchthat each patch may be described by a depth field with respect to aplane in 2D space. In some embodiments, the patch generation module(306) aims at decomposing the point cloud into a minimum number ofpatches with smooth boundaries, while also minimizing the reconstructionerror.

The patch info module (304) can collect the patch information thatindicates sizes and shapes of the patches. In some examples, the patchinformation can be packed into an image frame and then encoded by theauxiliary patch info compression module (338) to generate the compressedauxiliary patch information.

The patch packing module (308) is configured to map the extractedpatches onto a 2 dimensional (2D) grid while minimize the unused spaceand guarantee that every M×M (e.g., 16×16) block of the grid isassociated with a unique patch. Efficient patch packing can directlyimpact the compression efficiency either by minimizing the unused spaceor ensuring temporal consistency.

The geometry image generation module (310) can generate 2D geometryimages associated with geometry of the point cloud at given patchlocations. The texture image generation module (312) can generate 2Dtexture images associated with texture of the point cloud at given patchlocations. The geometry image generation module (310) and the textureimage generation module (312) exploit the 3D to 2D mapping computedduring the packing process to store the geometry and texture of thepoint cloud as images. In order to better handle the case of multiplepoints being projected to the same sample, each patch is projected ontotwo images, referred to as layers. In an example, geometry image isrepresented by a monochromatic frame of WxH in YUV420-8 bit format. Togenerate the texture image, the texture generation procedure exploitsthe reconstructed/smoothed geometry in order to compute the colors to beassociated with the re-sampled points.

The occupancy map module (314) can generate an occupancy map thatdescribes padding information at each unit. For example, the occupancyimage includes a binary map that indicates for each cell of the gridwhether the cell belongs to the empty space or to the point cloud. In anexample, the occupancy map uses binary information describing for eachpixel whether the pixel is padded or not. In another example, theoccupancy map uses binary information describing for each block ofpixels whether the block of pixels is padded or not.

The occupancy map generated by the occupancy map module (314) can becompressed using lossless coding or lossy coding. When lossless codingis used, the entropy compression module (334) is used to compress theoccupancy map. When lossy coding is used, the video compression module(332) is used to compress the occupancy map.

It is noted that the patch packing module (308) may leave some emptyspaces between 2D patches packed in an image frame. The image paddingmodules (316) and (318) can fill the empty spaces (referred to aspadding) in order to generate an image frame that may be suited for 2Dvideo and image codecs. The image padding is also referred to asbackground filling which can fill the unused space with redundantinformation. In some examples, a good background filling minimallyincreases the bit rate while does not introduce significant codingdistortion around the patch boundaries.

The video compression modules (322), (323), and (332) can encode the 2Dimages, such as the padded geometry images, padded texture images, andoccupancy maps based on a suitable video coding standard, such as HEVC,VVC and the like. In an example, the video compression modules (322),(323), and (332) are individual components that operate separately. Itis noted that the video compression modules (322), (323), and (332) canbe implemented as a single component in another example.

In some examples, the smoothing module (336) is configured to generate asmoothed image of the reconstructed geometry image. The smoothed imagecan be provided to the texture image generation (312). Then, the textureimage generation (312) may adjust the generation of the texture imagebased on the reconstructed geometry images. For example, when a patchshape (e.g. geometry) is slightly distorted during encoding anddecoding, the distortion may be taken into account when generating thetexture images to correct for the distortion in patch shape.

In some embodiments, the group dilation (320) is configured to padpixels around the object boundaries with redundant low-frequency contentin order to improve coding gain as well as visual quality ofreconstructed point cloud.

The multiplexer (324) can multiplex the compressed geometry image, thecompressed texture image, the compressed occupancy map, the compressedauxiliary patch information into a compressed bitstream.

FIG. 4 shows a block diagram of a V-PCC decoder (400) for decodingcompressed bitstream corresponding to point cloud frames, according tosome embodiments. In some embodiments, the V-PCC decoder (400) can beused in the communication system (100) and streaming system (200). Forexample, the decoder (210) can be configured to operate in a similarmanner as the V-PCC decoder (400). The V-PCC decoder (400) receives thecompressed bitstream, and generates reconstructed point cloud based onthe compressed bitstream.

In the FIG. 4 example, the V-PCC decoder (400) includes a de-multiplexer(432), video decompression modules (434) and (436), an occupancy mapdecompression module (438), an auxiliary patch-information decompressionmodule (442), a geometry reconstruction module (444), a smoothing module(446), a texture reconstruction module (448), and a color smoothingmodule (452).

The de-multiplexer (432) can receive and separate the compressedbitstream into compressed texture image, compressed geometry image,compressed occupancy map, and compressed auxiliary patch information.

The video decompression modules (434) and (436) can decode thecompressed images according to a suitable standard (e.g., HEVC, VVC,etc.) and output decompressed images. For example, the videodecompression module (434) decodes the compressed texture images andoutputs decompressed texture images; and the video decompression module(436) decodes the compressed geometry images and outputs thedecompressed geometry images.

The occupancy map decompression module (438) can decode the compressedoccupancy maps according to a suitable standard (e.g., HEVC, VVC, etc.)and output decompressed occupancy maps.

The auxiliary patch-information decompression module (442) can decodethe compressed auxiliary patch information according to a suitablestandard (e.g., HEVC, VVC, etc.) and output decompressed auxiliary patchinformation.

The geometry reconstruction module (444) can receive the decompressedgeometry images, and generate reconstructed point cloud geometry basedon the decompressed occupancy map and decompressed auxiliary patchinformation.

The smoothing module (446) can smooth incongruences at edges of patches.The smoothing procedure aims at alleviating potential discontinuitiesthat may arise at the patch boundaries due to compression artifacts. Insome embodiments, a smoothing filter may be applied to the pixelslocated on the patch boundaries to alleviate the distortions that may becaused by the compression/decompression.

The texture reconstruction module (448) can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothing geometry.

The color smoothing module (452) can smooth incongruences of coloring.Non-neighboring patches in 3D space are often packed next to each otherin 2D videos. In some examples, pixel values from non-neighboringpatches might be mixed up by the block-based video codec. The goal ofcolor smoothing is to reduce the visible artifacts that appear at patchboundaries.

FIG. 5 shows a block diagram of a video decoder (510) according to anembodiment of the present disclosure. The video decoder (510) can beused in the V-PCC decoder (400). For example, the video decompressionmodules (434) and (436), the occupancy map decompression module (438)can be similarly configured as the video decoder (510).

The video decoder (510) may include a parser (520) to reconstructsymbols (521) from compressed images, such as the coded video sequence.Categories of those symbols include information used to manage operationof the video decoder (510). The parser (520) may parse/entropy-decodethe coded video sequence that is received. The coding of the coded videosequence can be in accordance with a video coding technology orstandard, and can follow various principles, including variable lengthcoding, Huffman coding, arithmetic coding with or without contextsensitivity, and so forth. The parser (520) may extract from the codedvideo sequence, a set of subgroup parameters for at least one of thesubgroups of pixels in the video decoder, based upon at least oneparameter corresponding to the group. Subgroups can include Groups ofPictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units(CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and soforth. The parser (520) may also extract from the coded video sequenceinformation such as transform coefficients, quantizer parameter values,motion vectors, and so forth.

The parser (520) may perform an entropy decoding/parsing operation onthe video sequence received from a buffer memory, so as to createsymbols (521).

Reconstruction of the symbols (521) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (suchas: inter and intra picture, inter and intra block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (520). The flow of such subgroup control information between theparser (520) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, the video decoder (510)can be conceptually subdivided into a number of functional units asdescribed below. In a practical implementation operating undercommercial constraints, many of these units interact closely with eachother and can, at least partly, be integrated into each other. However,for the purpose of describing the disclosed subject matter, theconceptual subdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (551). Thescaler/inverse transform unit (551) receives a quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (521) from the parser (520). The scaler/inversetransform unit (551) can output blocks comprising sample values that canbe input into aggregator (555).

In some cases, the output samples of the scaler/inverse transform (551)can pertain to an intra coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by an intrapicture prediction unit (552). In some cases, the intra pictureprediction unit (552) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current picture buffer (558). The currentpicture buffer (558) buffers, for example, partly reconstructed currentpicture and/or fully reconstructed current picture. The aggregator(555), in some cases, adds, on a per sample basis, the predictioninformation the intra prediction unit (552) has generated to the outputsample information as provided by the scaler/inverse transform unit(551).

In other cases, the output samples of the scaler/inverse transform unit(551) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a motion compensation prediction unit (553) canaccess reference picture memory (557) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (521) pertaining to the block, these samples can beadded by the aggregator (555) to the output of the scaler/inversetransform unit (551) (in this case called the residual samples orresidual signal) so as to generate output sample information. Theaddresses within the reference picture memory (557) from where themotion compensation prediction unit (553) fetches prediction samples canbe controlled by motion vectors, available to the motion compensationprediction unit (553) in the form of symbols (521) that can have, forexample X, Y, and reference picture components. Motion compensation alsocan include interpolation of sample values as fetched from the referencepicture memory (557) when sub-sample exact motion vectors are in use,motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (555) can be subject to variousloop filtering techniques in the loop filter unit (556). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video sequence (alsoreferred to as coded video bitstream) and made available to the loopfilter unit (556) as symbols (521) from the parser (520), but can alsobe responsive to meta-information obtained during the decoding ofprevious (in decoding order) parts of the coded picture or coded videosequence, as well as responsive to previously reconstructed andloop-filtered sample values.

The output of the loop filter unit (556) can be a sample stream that canbe output to a render device as well as stored in the reference picturememory (557) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. For example, once a codedpicture corresponding to a current picture is fully reconstructed andthe coded picture has been identified as a reference picture (by, forexample, the parser (520)), the current picture buffer (558) can becomea part of the reference picture memory (557), and a fresh currentpicture buffer can be reallocated before commencing the reconstructionof the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology in a standard, such as ITU-TRec. H.265. The coded video sequence may conform to a syntax specifiedby the video compression technology or standard being used, in the sensethat the coded video sequence adheres to both the syntax of the videocompression technology or standard and the profiles as documented in thevideo compression technology or standard. Specifically, a profile canselect certain tools as the only tools available for use under thatprofile from all the tools available in the video compression technologyor standard. Also necessary for compliance can be that the complexity ofthe coded video sequence is within bounds as defined by the level of thevideo compression technology or standard. In some cases, levels restrictthe maximum picture size, maximum frame rate, maximum reconstructionsample rate (measured in, for example megasamples per second), maximumreference picture size, and so on. Limits set by levels can, in somecases, be further restricted through Hypothetical Reference Decoder(HRD) specifications and metadata for HRD buffer management signaled inthe coded video sequence.

FIG. 6 shows a block diagram of a video encoder (603) according to anembodiment of the present disclosure. The video encoder (603) can beused in the V-PCC encoder (300) that compresses point clouds. In anexample, the video compression module (322) and (323), and the videocompression module (332) are configured similarly to the encoder (603).

The video encoder (603) may receive images, such as padded geometryimages, padded texture images and the like, and generate compressedimages.

According to an embodiment, the video encoder (603) may code andcompress the pictures of the source video sequence (images) into a codedvideo sequence (compressed images) in real time or under any other timeconstraints as required by the application. Enforcing appropriate codingspeed is one function of a controller (650). In some embodiments, thecontroller (650) controls other functional units as described below andis functionally coupled to the other functional units. The coupling isnot depicted for clarity. Parameters set by the controller (650) caninclude rate control related parameters (picture skip, quantizer, lambdavalue of rate-distortion optimization techniques, . . . ), picture size,group of pictures (GOP) layout, maximum motion vector search range, andso forth. The controller (650) can be configured to have other suitablefunctions that pertain to the video encoder (603) optimized for acertain system design.

In some embodiments, the video encoder (603) is configured to operate ina coding loop. As an oversimplified description, in an example, thecoding loop can include a source coder (630) (e.g., responsible forcreating symbols, such as a symbol stream, based on an input picture tobe coded, and a reference picture(s)), and a (local) decoder (633)embedded in the video encoder (603). The decoder (633) reconstructs thesymbols to create the sample data in a similar manner as a (remote)decoder also would create (as any compression between symbols and codedvideo bitstream is lossless in the video compression technologiesconsidered in the disclosed subject matter). The reconstructed samplestream (sample data) is input to the reference picture memory (634). Asthe decoding of a symbol stream leads to bit-exact results independentof decoder location (local or remote), the content in the referencepicture memory (634) is also bit exact between the local encoder andremote encoder. In other words, the prediction part of an encoder “sees”as reference picture samples exactly the same sample values as a decoderwould “see” when using prediction during decoding. This fundamentalprinciple of reference picture synchronicity (and resulting drift, ifsynchronicity cannot be maintained, for example because of channelerrors) is used in some related arts as well.

The operation of the “local” decoder (633) can be the same as of a“remote” decoder, such as the video decoder (510), which has alreadybeen described in detail above in conjunction with FIG. 5 . Brieflyreferring also to FIG. 5 , however, as symbols are available andencoding/decoding of symbols to a coded video sequence by an entropycoder (645) and the parser (520) can be lossless, the entropy decodingparts of the video decoder (510), including and parser (520) may not befully implemented in the local decoder (633).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. For this reason, thedisclosed subject matter focuses on decoder operation. The descriptionof encoder technologies can be abbreviated as they are the inverse ofthe comprehensively described decoder technologies. Only in certainareas a more detail description is required and provided below.

During operation, in some examples, the source coder (630) may performmotion compensated predictive coding, which codes an input picturepredictively with reference to one or more previously-coded picture fromthe video sequence that were designated as “reference pictures”. In thismanner, the coding engine (632) codes differences between pixel blocksof an input picture and pixel blocks of reference picture(s) that may beselected as prediction reference(s) to the input picture.

The local video decoder (633) may decode coded video data of picturesthat may be designated as reference pictures, based on symbols createdby the source coder (630). Operations of the coding engine (632) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 6 ), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (633) replicates decodingprocesses that may be performed by the video decoder on referencepictures and may cause reconstructed reference pictures to be stored inthe reference picture cache (634). In this manner, the video encoder(603) may store copies of reconstructed reference pictures locally thathave common content as the reconstructed reference pictures that will beobtained by a far-end video decoder (absent transmission errors).

The predictor (635) may perform prediction searches for the codingengine (632). That is, for a new picture to be coded, the predictor(635) may search the reference picture memory (634) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(635) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (635), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (634).

The controller (650) may manage coding operations of the source coder(630), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (645). The entropy coder (645)translates the symbols as generated by the various functional units intoa coded video sequence, by lossless compressing the symbols according totechnologies such as Huffman coding, variable length coding, arithmeticcoding, and so forth.

The controller (650) may manage operation of the video encoder (603).During coding, the controller (650) may assign to each coded picture acertain coded picture type, which may affect the coding techniques thatmay be applied to the respective picture. For example, pictures oftenmay be assigned as one of the following picture types:

An Intra Picture (I picture) may be one that may be coded and decodedwithout using any other picture in the sequence as a source ofprediction. Some video codecs allow for different types of intrapictures, including, for example Independent Decoder Refresh (“IDR”)Pictures. A person skilled in the art is aware of those variants of Ipictures and their respective applications and features.

A predictive picture (P picture) may be one that may be coded anddecoded using intra prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A bi-directionally predictive picture (B Picture) may be one that may becoded and decoded using intra prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (for example, blocks of 4×4, 8×8, 4×8, or 16×16 sampleseach) and coded on a block-by-block basis. Blocks may be codedpredictively with reference to other (already coded) blocks asdetermined by the coding assignment applied to the blocks' respectivepictures. For example, blocks of I pictures may be codednon-predictively or they may be coded predictively with reference toalready coded blocks of the same picture (spatial prediction or intraprediction). Pixel blocks of P pictures may be coded predictively, viaspatial prediction or via temporal prediction with reference to onepreviously coded reference picture. Blocks of B pictures may be codedpredictively, via spatial prediction or via temporal prediction withreference to one or two previously coded reference pictures.

The video encoder (603) may perform coding operations according to apredetermined video coding technology or standard, such as ITU-T Rec.H.265. In its operation, the video encoder (603) may perform variouscompression operations, including predictive coding operations thatexploit temporal and spatial redundancies in the input video sequence.The coded video data, therefore, may conform to a syntax specified bythe video coding technology or standard being used.

A video may be in the form of a plurality of source pictures (images) ina temporal sequence. Intra-picture prediction (often abbreviated tointra prediction) makes use of spatial correlation in a given picture,and inter-picture prediction makes uses of the (temporal or other)correlation between the pictures. In an example, a specific pictureunder encoding/decoding, which is referred to as a current picture, ispartitioned into blocks. When a block in the current picture is similarto a reference block in a previously coded and still buffered referencepicture in the video, the block in the current picture can be coded by avector that is referred to as a motion vector. The motion vector pointsto the reference block in the reference picture, and can have a thirddimension identifying the reference picture, in case multiple referencepictures are in use.

In some embodiments, a bi-prediction technique can be used in theinter-picture prediction. According to the bi-prediction technique, tworeference pictures, such as a first reference picture and a secondreference picture that are both prior in decoding order to the currentpicture in the video (but may be in the past and future, respectively,in display order) are used. A block in the current picture can be codedby a first motion vector that points to a first reference block in thefirst reference picture, and a second motion vector that points to asecond reference block in the second reference picture. The block can bepredicted by a combination of the first reference block and the secondreference block.

Further, a merge mode technique can be used in the inter-pictureprediction to improve coding efficiency.

According to some embodiments of the disclosure, predictions, such asinter-picture predictions and intra-picture predictions are performed inthe unit of blocks. For example, according to the HEVC standard, apicture in a sequence of video pictures is partitioned into coding treeunits (CTU) for compression, the CTUs in a picture have the same size,such as 64×64 pixels, 32×32 pixels, or 16×16 pixels. In general, a CTUincludes three coding tree blocks (CTBs), which are one luma CTB and twochroma CTBs. Each CTU can be recursively quadtree split into one ormultiple coding units (CUs). For example, a CTU of 64×64 pixels can besplit into one CU of 64×64 pixels, or 4 CUs of 32×32 pixels, or 16 CUsof 16×16 pixels. In an example, each CU is analyzed to determine aprediction type for the CU, such as an inter prediction type or an intraprediction type. The CU is split into one or more prediction units (PUs)depending on the temporal and/or spatial predictability. Generally, eachPU includes a luma prediction block (PB), and two chroma PBs. In anembodiment, a prediction operation in coding (encoding/decoding) isperformed in the unit of a prediction block. Using a luma predictionblock as an example of a prediction block, the prediction block includesa matrix of values (e.g., luma values) for pixels, such as 8×8 pixels,16×16 pixels, 8×16 pixels, 16×8 pixels, and the like.

FIG. 7 shows a block diagram of a G-PPC encoder (700) in accordance withsome embodiments. The encoder (700) can be configured to receive pointcloud data and compress the point cloud data to generate a bit streamcarrying compressed point cloud data. In an embodiment, the encoder(700) can include a position quantization module (710), a duplicatedpoints removal module (712), an octree encoding module (730), anattribute transfer module (720), a level of detail (LOD) generationmodule (740), an attribute prediction module (750), a residualquantization module (760), an arithmetic coding module (770), an inverseresidual quantization module (780), an addition module (781), and amemory (790) to store reconstructed attribute values.

As shown, an input point cloud (701) can be received at the encoder(700). Positions (e.g., 3D coordinates) of the point cloud (701) areprovided to the quantization module (710). The quantization module (710)is configured to quantize the coordinates to generate quantizedpositions. The duplicated points removal module (712) is configured toreceive the quantized positions and perform a filter process to identifyand remove duplicated points. The octree encoding module (730) isconfigured to receive filtered positions from the duplicated pointsremoval module (712), and perform an octree-based encoding process togenerate a sequence of occupancy codes that describe a 3D grid ofvoxels. The occupancy codes are provided to the arithmetic coding module(770).

The attribute transfer module (720) is configured to receive attributesof the input point cloud, and perform an attribute transfer process todetermine an attribute value for each voxel when multiple attributevalues are associated with the respective voxel. The attribute transferprocess can be performed on the re-ordered points output from the octreeencoding module (730). The attributes after the transfer operations areprovided to the attribute prediction module (750). The LOD generationmodule (740) is configured to operate on the re-ordered points outputfrom the octree encoding module (730), and re-organize the points intodifferent LODs. LOD information is supplied to the attribute predictionmodule (750).

The attribute prediction module (750) processes the points according toan LOD-based order indicated by the LOD information from the LODgeneration module (740). The attribute prediction module (750) generatesan attribute prediction for a current point based on reconstructedattributes of a set of neighboring points of the current point stored inthe memory (790). Prediction residuals can subsequently be obtainedbased on original attribute values received from the attribute transfermodule (720) and locally generated attribute predictions. When candidateindices are used in the respective attribute prediction process, anindex corresponding to a selected prediction candidate may be providedto the arithmetic coding module (770).

The residual quantization module (760) is configured to receive theprediction residuals from the attribute prediction module (750), andperform quantization to generate quantized residuals. The quantizedresiduals are provided to the arithmetic coding module (770).

The inverse residual quantization module (780) is configured to receivethe quantized residuals from the residual quantization module (760), andgenerate reconstructed prediction residuals by performing an inverse ofthe quantization operations performed at the residual quantizationmodule (760). The addition module (781) is configured to receive thereconstructed prediction residuals from the inverse residualquantization module (780), and the respective attribute predictions fromthe attribute prediction module (750). By combining the reconstructedprediction residuals and the attribute predictions, the reconstructedattribute values are generated and stored to the memory (790).

The arithmetic coding module (770) is configured to receive theoccupancy codes, the candidate indices (if used), the quantizedresiduals (if generated), and other information, and perform entropyencoding to further compress the received values or information. As aresult, a compressed bitstream (702) carrying the compressed informationcan be generated. The bitstream (702) may be transmitted, or otherwiseprovided, to a decoder that decodes the compressed bitstream, or may bestored in a storage device.

FIG. 8 shows a block diagram of a G-PCC decoder (800) in accordance withan embodiment. The decoder (800) can be configured to receive acompressed bitstream and perform point cloud data decompression todecompress the bitstream to generate decoded point cloud data. In anembodiment, the decoder (800) can include an arithmetic decoding module(810), an inverse residual quantization module (820), an octree decodingmodule (830), an LOD generation module (840), an attribute predictionmodule (850), and a memory (860) to store reconstructed attributevalues.

As shown, a compressed bitstream (801) can be received at the arithmeticdecoding module (810). The arithmetic decoding module (810) isconfigured to decode the compressed bitstream (801) to obtain quantizedresiduals (if generated) and occupancy codes of a point cloud. Theoctree decoding module (830) is configured to determine reconstructedpositions of points in the point cloud according to the occupancy codes.The LOD generation module (840) is configured to re-organize the pointsinto different LODs based on the reconstructed positions, and determinean LOD-based order. The inverse residual quantization module (820) isconfigured to generate reconstructed residuals based on the quantizedresiduals received from the arithmetic decoding module (810).

The attribute prediction module (850) is configured to perform anattribute prediction process to determine attribute predictions for thepoints according to the LOD-based order. For example, an attributeprediction of a current point can be determined based on reconstructedattribute values of neighboring points of the current point stored inthe memory (860). In some examples, the attribute prediction can becombined with a respective reconstructed residual to generate areconstructed attribute for the current point.

A sequence of reconstructed attributes generated from the attributeprediction module (850) together with the reconstructed positionsgenerated from the octree decoding module (830) corresponds to a decodedpoint cloud (802) that is output from the decoder (800) in one example.In addition, the reconstructed attributes are also stored into thememory (860) and can be subsequently used for deriving attributepredictions for subsequent points.

In various embodiments, the encoder (300), the decoder (400), theencoder (700), and/or the decoder (800) can be implemented withhardware, software, or combination thereof. For example, the encoder(300), the decoder (400), the encoder (700), and/or the decoder (800)can be implemented with processing circuitry such as one or moreintegrated circuits (ICs) that operate with or without software, such asan application specific integrated circuit (ASIC), field programmablegate array (FPGA), and the like. In another example, the encoder (300),the decoder (400), the encoder (700), and/or the decoder (800) can beimplemented as software or firmware including instructions stored in anon-volatile (or non-transitory) computer-readable storage medium. Theinstructions, when executed by processing circuitry, such as one or moreprocessors, causing the processing circuitry to perform functions of theencoder (300), the decoder (400), the encoder (700), and/or the decoder(800).

It is noted that the attribute prediction modules (750) and (850)configured to implement the attribute prediction techniques disclosedherein can be included in other decoders or encoders that may havesimilar or different structures from what is shown in FIG. 7 and FIG. 8. In addition, the encoder (700) and decoder (800) can be included in asame device, or separate devices in various examples.

Aspects of the disclosure provide techniques for use in G-PCC.Specifically, node-based geometry and/or attribute coding techniques forG-PCC are described in the following description.

According to some aspects of the disclosure, geometry information andthe associated attributes of a point cloud, such as color, reflectanceand the like can be separately compressed (e.g., in the MPEG G-PCC TestModel 13 (TMC13) model). The geometry information of the point cloud,which includes the 3D coordinates of the points in the point cloud, canbe coded by partitions with occupancy information of the partitions,such as an octree partition, quadtree-partition and binary partitionwith occupancy information of the partitions. After the geometryinformation is coded, the attributes can be compressed based on areconstructed geometry using, for example, prediction, lifting andregion adaptive hierarchical transform techniques.

For geometry coding, several techniques can be used. The severaltechniques can include tree based geometry coding, predictive tree basedgeometry coding and trisoup based geometry coding. The tree basedgeometry coding, predictive tree based geometry coding and trisoup basedgeometry coding will be described respectively.

According to an aspect of the disclosure, a three dimensional space canbe partitioned using tree partition, such as octree partition, quadtreepartition, binary tree partition and the like. Using octree partition asan example, octrees are the three dimensional analog of quadtrees in thetwo dimensional space. Octree partition technique refers to thepartition technique that recursively subdivides three dimensional spaceinto eight octants, and an octree structure refers to the tree structurethat represents the partitions. In an example, each node in the octreestructure corresponds to a three dimensional space, and the node can bean end node (no more partition, also referred to as leaf node in someexamples) or a node with a further partition. An octree partition at anode can partition the three dimensional space represented by the nodeinto eight octants. In some examples, nodes corresponding to partitionsof a specific node can be referred to as child nodes of the specificnode.

FIG. 9 shows a diagram illustrating a partition of a 3D cube (900)(corresponding to a node) based on the octree partition techniqueaccording to some embodiments of the disclosure. The partition candivide the 3D cube (900) into eight smaller equal-sized cubes 0-7 asshown in FIG. 9 . In the FIG. 9 , the x, y and z dimensions of the 3Dcube (900) are respectively divided into half and division can result 8sub-cubes with the same size.

The octree partition technique (e.g., in TMC13) can recursively dividean original 3D space into the smaller units, and the occupancyinformation of every sub-space can be encoded to represent geometrypositions.

In some embodiments (e.g., In TMC13), an octree geometry codec is used.The octree geometry codec can perform geometry encoding. In someexamples, geometry encoding is performed on a cubical box. For example,the cubical box can be an axis-aligned bounding box B that is defined bytwo points (0,0,0) and (2^(M−1),2^(M−1),2^(M−1)), where 2^(M−1) definesthe size of the bounding box B and M can be specified in the bitstream.

Then, an octree structure is built by recursively subdividing thecubical box. For example, the cubical box defined by the two points(0,0,0) and (2^(M−1),2^(M−1),2^(M−1)) is divided into 8 sub cubicalboxes, then an 8-bit code, that is referred to as an occupancy code, isgenerated. Each bit of the occupancy code is associated with a subcubical box, and the value of the bit is used to indicate whether theassociated sub cubical box contains any points of the point cloud. Forexample, value 1 of a bit indicates that the sub cubical box associatedwith the bit contains one or more points of the point cloud; and value 0of a bit indicates that the sub cubical box associated with the bitcontains no point of the point cloud.

Further, for empty sub cubical box (e.g., the value of the bitassociated with the sub cubical box is 0), no more division is appliedon the sub cubical box. When a sub cubical box has one or more points ofthe point cloud (e.g., the value of the bit associated with the subcubical box is 1), the sub cubical box is further divided into 8 smallersub cubical boxes, and an occupancy code can be generated for the subcubical box to indicate the occupancy of the smaller sub cubical boxes.In some examples, the subdivision operations can be repetitivelyperformed on non-empty sub cubical boxes until the size of the subcubical boxes is equal to a predetermined threshold, such as sizebeing 1. In some examples, the sub cubical boxes with a size of 1 arereferred to as voxels, and the sub cubical boxes that have larger sizesthan voxels can be referred to as non-voxels.

FIG. 10 shows an example of an octree partition (1010) and an octreestructure (1020) corresponding to the octree partition (1010) accordingto some embodiments of the disclosure. FIG. 10 shows two levels ofpartitions in the octree partition (1010). The octree structure (1020)includes a node (NO) corresponding to the cubical box for octreepartition (1010). The node NO is at depth 0 of the octree structure(1020). At a first level of partition, the cubical box is partitionedinto 8 sub cubical boxes that are numbered 0-7 according to thenumbering technique shown in FIG. 9 . The occupancy code for thepartition of the node NO is “10000001” in binary, which indicates thefirst sub cubical box represented by node N0-0 and the eighth subcubical box represented by node N0-7 includes points in the point cloudand other sub cubical boxes are empty. The nodes N0-0 to N0-7 are atdepth 1 of the octree structure (1020).

Then, at the second level of partition, the first sub cubical box(represented by node N0-0) and the eighth sub cubical box (representedby node N0-7) are further respectively sub-divided into eight octants.For example, the first sub cubical box (represented by node N0-0) ispartitioned into 8 smaller sub cubical boxes that are numbered 0-7according to the numbering technique shown in FIG. 9 . The occupancycode for the partition of the node N0-0 is “00011000” in binary, whichindicates the fourth smaller sub cubical box (represented by nodeN0-0-3) and the fifth smaller sub cubical box (represented by nodeN0-0-4) includes points in the point cloud and other smaller sub cubicalboxes are empty. The nodes N0-0-0 to N0-0-7 are at depth 2 of the octreestructure (1020). At the second level, the eighth sub cubical box(represented by node N0-7) is similarly partitioned into 8 smaller subcubical boxes as shown in FIG. 10 . The nodes N0-7-0 to N0-7-7 are atdepth 2 of the octree structure (1020).

In the FIG. 10 example, the nodes corresponding to non-empty cubicalspace (e.g., cubical box, sub cubical boxes, smaller sub cubical boxesand the like) are colored in grey, and referred to as shaded nodes. Thenodes corresponding to empty cubical space (e.g., cubical box, subcubical boxes, smaller sub cubical boxes and the like) are colored inwhite, and can be referred to as blank nodes.

While octree partition is described in the above description, generallytree based geometry coding technique can partition a point cloud usingother partitions, such as quadtree or binary tree partitions.

More generally, for a point cloud, the bounding box B of the point cloudis not restricted to be with same size in all directions, instead thebounding box B can be arbitrary-size rectangular cuboid to better fitthe shape of the 3D scene or objects. In some examples, the size of thebounding box B can be represented as a power of two, such as (2 ^(d)^(x) ,2 ^(d) ^(y) ,2 ^(d) ^(z) ). In an example, d_(x),d_(y),d_(z) arenot equal.

To partition a bounding box B of a point cloud, in addition to theoctree partition in the above description, quadtree partition and binarytree partition in the following description can be used.

FIG. 11 shows three quadtree partitions in some examples. For a quadtreepartition, two of three dimensions (e.g., x, y, and z dimensions) of abounding box B can be splitting into half, and the quadtree partitioncan result 4 sub-boxes with the same size.

In the FIG. 11 example, a bounding box (1110) is split by quadtreepartition in the x and y dimensions, the result of the quadtreepartition is shown by 4 sub-boxes with the same size that are labeled as0, 2, 4, and 6.

Further, in the FIG. 11 example, a bounding box (1120) is split byquadtree partition in the x and z dimensions, the result of the quadtreepartition is shown by 4 sub-boxes with the same size that are labeled as0, 1, 4, and 5.

Further, in the FIG. 11 example, a bounding box (1130) is split byquadtree partition in the y and z dimensions, the result of the quadtreepartition is shown by 4 sub-boxes with the same size that are labeled as0, 1, 2, and 3.

FIG. 12 shows three binary tree partitions in some examples. For abinary tree partition, one of three dimensions (e.g., x, y, and zdimensions) of a bounding box B can be splitting into half, and thebinary tree partition can result 2 sub-boxes with the same size.

In the FIG. 12 example, a bounding box (1210) is split by binary treepartition in the x dimension, the result of the binary partition isshown by 2 sub-boxes with the same size that are labeled as 0, and 4.

Further, in the FIG. 12 example, a bounding box (1220) is split bybinary tree partition in the y dimension, the result of the binarypartition is shown by 2 sub-boxes with the same size that are labeled as0, and 2.

Further, in the FIG. 12 example, a bounding box (1230) is split bybinary tree partition in the z dimension, the result of the binarypartition is shown by 2 sub-boxes with the same size that are labeled as0, and 1.

Accordingly, a point cloud can be represented by a general treestructure with a suitable mix of octree partition, quadtree partitionand binary tree partition. To traverse a tree structure, in someexamples (e.g., a version of TMC13 model), a breadth-first approach isadopted. In some other examples, a depth-first approach can also beused.

In some related examples, (e.g., a version of TMC13), to code theoccupancy codes, the tree structure is traversed in the breadth firstorder. According to the breadth first order, tree nodes (e.g., nodes inthe tree structure) in a level can be visited after all of the treenodes in an upper level have been visited. In an implementation example,a first-in-first-out (FIFO) data structure can be used.

FIG. 13 shows a diagram of an octree structure (1300) illustratingbreadth first coding order. The shaded nodes in the octree structure(1300) are nodes corresponding to cubical spaces that are not empty andare occupied by one or more points in the point cloud. The occupancycodes for the shaded nodes can be coded in the breadth first codingorder from 0 to 8 shown in FIG. 13 . In the breadth first coding order,the octree nodes are visited level-by-level. According to an aspect ofthe disclosure, the breadth first coding order by itself is not suitablefor parallel processing because the current level has to wait for theupper level to be coded.

In some examples, a hybrid coding order that includes at least one levelthat is coded using a depth first coding order instead of the breadthfirst coding order. Thus, in some embodiments, a node at the level withthe depth first coding order and descendant nodes of the node can form asub tree structure of the tree structure. When the level with depthfirst coding order includes multiple nodes respectively corresponding tonon-empty cubical spaces, the multiple nodes and their correspondingdescendant nodes can form multiple sub tree structures. The multiple subtree structures can be coded in parallel in some embodiments.

FIG. 14 shows a diagram of an octree structure (1400) illustrating depthfirst coding order. The shaded nodes in the octree structure (1400) arenodes corresponding to cubical spaces that are not empty. The octreestructure (1400) can correspond to same occupancy geometry of a pointcloud as the octree structure (1300). The occupancy codes for the shadednodes can be coded in the depth first coding order from 0 to 8 shown inFIG. 14 .

In the FIG. 14 example, node “0” can be at any suitable partition depth,such as PD0, child nodes of the node “0” are at the partition depthPD0+1, and grandchild nodes of the node “0” are at the partition depthPD0+2. In the FIG. 14 example, nodes at partition depth PD0+1 can becoded in a depth first coding order. The nodes at the partition depthPD0+1 include two nodes that correspond to non-empty space. The twonodes and their respectively descendant nodes can form a first suboctree structure (1410) and a second sub octree structure (1420), thetwo nodes can be respectively referred to as root nodes of the two suboctree structures.

The depth first coding order in FIG. 14 is referred to as a preorderversion of the depth first coding order. In the preorder version of thedepth first coding order, for each sub octree structure, the root nodeof the sub octree is visited first before visiting the child nodes ofthe sub octree structure. Further, the deepest node is first visited andthen track back to the siblings of the parent node.

In the FIG. 14 example, the first sub octree structure (1410) and thesecond sub octree structure (1420) can be coded in parallel processingin some implementations. For example, node 1 and node 5 can be visitedat the same time. In some examples, recursion programming or stack datastructure can be used to implement the depth first coding order.

In some embodiments, the hybrid coding order starts with the breadthfirst traversing (coding), and after several levels of breadth firsttraversing, the depth-first traversing (coding) can be enabled.

In some examples, predictive tree based geometry coding can be used.Predictive tree based geometry coding can be used when a predictivetree, such as a spanning tree can be constructed over all the points inthe point cloud. In an example, for a prediction of a point, allancestors can be used.

FIG. 15 shows a portion of a predictive tree (1510) that spans a pointcloud (1500) of a rabbit. In some examples, the position of a point inthe predictive tree (1510) can be predicted from the position of itsparent point, or from the positions of its parent and its grandparentpoint.

In some examples, trisoup based geometry coding can be used. The trisoupbased geometry coding approach can represent surfaces of an object as aseries of triangle mesh. In an example, the trisoup based geometrycoding approach is applied for a dense surface point cloud. A decoderusing the trisoup based geometry coding can generate a point cloud fromthe mesh surface in the specified voxel granularity to assure thedensity of the reconstructed point cloud. According to an aspect of thedisclosure, trisoup based geometry coding can introduce distortion tothe original point cloud, but may provide the benefit of reducedbitstream size.

For attribute coding, several techniques can be used. The techniquesinclude prediction based attribute coding, lifting based attributecoding, region adaptive hierarchical transform (RAHT) based attributecoding and the like. The prediction based attribute coding, liftingbased attribute coding, and RAHT based attribute coding and the likewill be respectively described in the following description.

For prediction based attribute coding, let (P_(i))_(i=l) _(N) denote aset of positions associated with points in a point cloud. For eachposition that is represented by a multi dimensional data, a Morton codeof one dimension can be determined to be associated with the position.Let (M_(i))_(i=l) _(N) denote the Morton codes respectively associatedwith positions (P_(i))_(i=l) _(N) . The prediction based attributecoding includes a sorting step followed by a coding step. In the sortingstep, the points in the point cloud are sorted according to theassociated Morton codes in for example an ascending order. For example,let I denote an array of indexes for the points that are orderedaccording to the sorting step.

In the coding step, the encoder/decoder can compress/decompressrespectively the points according to the order defined by I initerations. At each iteration i, a point P_(i) is selected following theorder defined by I. The distances of P_(i) to a number of previouspoints in the order analyzed. For example, s (e.g., s=64) denotes thenumber of previous points that are analyzed. Based on the analyzed, k(e.g., k=3) nearest neighbors of P_(i) are selected to be used forattribute prediction. Specifically, in some examples, an attribute(α_(i)) of a point i can be predicted by using a linear interpolationprocess of attributes of k nearest neighbors (α_(h))_(h∈∪) _(k-1) thatare weighted based on the distances of the nearest neighbours to thepoint i. In an example, at a decoder side, let

denote the set of the k-nearest neighbours of the current point i, let

denote decoded/reconstructed attribute values of the neighbours and let(δ_(t))_(t∈)

_(i) denote their distances to the current point i. Then, the predictedattribute value {circumflex over (α)}₁ for the current point i can becalculated based on attribute values of the neighbors and theirdistances to the current point according to Eq. (1):

$\begin{matrix}{â_{i} = {{Round}\left( {\sum\limits_{j \in}{\aleph_{i}\frac{\frac{1}{\delta_{j}^{2}}}{{\sum t} \in {\aleph_{i}\frac{1}{\delta_{j}^{2}}}}{\overset{\sim}{a}}_{j}}} \right)}} & {{Eq}.(1)}\end{matrix}$

For lifting based attribute coding, additional steps are applied uponthe prediction-based coding. In an example, two additional steps thatare referred to as a first step of an update operator and a second stepof an adaptive quantization are used.

FIG. 16 shows a diagram of using a direct/forward transform architecture(1600) in the lifting based attribute coding at an encoder side ofG-PCC. In some examples, to facilitate prediction and update, thedirect/forward transform architecture (1600) includes multipleprediction/update (P/U) decompose stages to decompose an attributesignal. At each of the multiple P/U decompose stages, a signal(approximation signal) from previous stage is split into two sets ofhigh-correlation. In an example, in the lifting based attribute codingscheme of G-PCC, the splitting is performed by leveraging level ofdetails (LoD) structure in which such high-correlation is expected amonglevels and each level is constructed by a nearest neighbor search toorganize non-uniform point clouds into a structured data. A P/Udecomposition stage at a stage (N) results in a detail signal D(N) andan approximation signal L′(N), which is further decomposed into D(N−1)and L′(N−1) in a next stage (e.g., stage (N−1)). The decomposition isrepeatedly applied until a base layer approximation signal L′(0) isobtained in an example. Consequently, instead of coding an inputattribute signal itself that consists of various levels of details,detail signals D(N), D(N−1), . . . , D(0), and the base layerapproximation signal L′(0) can be coded in the lifting based attributecoding scheme.

It is noted that the application of P/U decomposition stages can resultin sparse sub-bands in the coefficients in D(N), . . . , D(0), therebyproviding a transform coding gain advantage.

FIG. 17 shows a diagram of using an inverse transform architecture(1700) in the lifting based attribute coding at a decoder side of G-PCC.The inverse transform architecture (1700) includes multipleprediction/update (P/U) merge stages to reconstruct an attribute signal.At each P/U merge stage, a detail signal is decoded and merged with anapproximation signal from a previous stage to generate a newapproximation signal for providing to the next P/U merge stage.

For RAHT based attribute coding, adaptive hierarchical transform can beused.

FIG. 18 shows a diagram of a RAHT forward transform architecture (1810)that can be used in an encoder side of G-PCC and a diagram of a RAHTinverse transform architecture (1820) that can be used in a decoder sideof G-PCC. In the FIG. 18 example,

${a^{2} = {{\frac{w_{0}}{w_{0} + w_{1}}{and}b^{2}} = \frac{w_{1}}{w_{0} + w_{1}}}},$and w₀ is the notation of the weight of the input coefficient F_(l+1,2n)while w₁ is the same for F_(l+l,2n+1).

According to some aspects of the disclosure, geometry and attributecoding for a point cloud can be performed in the manner of a node-basedgeometry and attribute coding. In tree-based geometry coding, a pointcloud is represented as a general tree structure, for example, usingoctree partition, quad-tree and binary tree partitions. The root node ofthe tree structure corresponds to the whole volume of the point cloudwhile the intermediate nodes of the tree structure correspond tosub-volumes of the point cloud (or sub-trees of the tree structure).

Referring back to FIG. 10 , the octree structure (1020) is used as anexample of a tree structure. For convenience, the node N0 is a root nodeand at depth 0 of the tree structure. A partition at a node can resultnodes with larger depth (e.g., depth increases by one). For example, apartition of a box corresponding to the node N0 can result nodes atdepth 1 of the tree structure. A partition of a sub-box at depth (k−1)can result nodes at depth k of the tree structure. Partitions can beperformed until all the nodes are unit nodes, for example, size in allthree dimensions is 1 in some examples.

In some embodiments, instead of coding attributes after the geometrycoding of the whole point cloud is completed, attributes can be coded inbetween geometry coding of the point cloud. In some examples, thegeometry of a point cloud can be coded until depth k is reached, where kis a positive integer and can be specified by an encoder and transmittedin the coded bitstream. In a first example, the geometry coding andattribute coding can be performed in a sub-volume based interleavemanner at depth k. Specifically, for example, for each occupied node atdepth k, which corresponds to a sub-volume (or subtree) of the pointcloud, the geometry information of points in the sub-volume (or nodes inthe subtree) can be encoded first, and attribute information of thepoints in the sub-volume (or the nodes in the subtree) can be encodedfollowing the geometry information of the points (or the nodes in thesubtree). Then, the encoding process can go to a next occupied node atdepth k. Thus, attribute information of a first occupied node at depth k(e.g., attribute information of points in a first sub volumecorresponding to the first occupied node) is encoded before geometryinformation of a second occupied node at depth k (e.g., geometryinformation of points in a second sub volume corresponding to the secondoccupied node) in the coded bitstream.

In a second example, the geometry coding and attribute coding can beperformed in a node based interleaved manner from depth k. For example,for each node at depth k and depth deeper than k, the geometryinformation of the node can be encoded first, and attribute informationof the node can be encoded following the geometry information of thenode. Then, the encoding process can go to a next node a depth k ordepth deeper than k. Thus, attribute information of a first node isencoded before geometry information of a second node in the codedbitstream.

In the first example of the sub-volume based interleave manner at depthk and the second example of the node based interleaved manner, the nodesat depth k can be treated as the top-level coding unit, in a similarmanner as the largest coding unit (LCU) used in HEVC video codingstandard. In some examples, in the field of the point cloud coding, eachnode at depth k form a separate subtree and each subtree can be referredto as an LCU in point cloud coding. The point cloud can be coded usingLCU based coding. The LCU based coding can be referred to as node basedcoding in some examples. The LCU based coding can include LCU basedgeometry coding, and LCU based geometry and attribute coding. The LCUbased geometry coding can code geometry information based on LCUs. TheLCU based geometry and attribute coding can code geometry informationand attribute information based on LCUs, such as in the sub-volume basedinterleave manner, in the node based interleave manner, and the like.

FIG. 19 shows a tree structure (1900) corresponding to a point cloudaccording to some examples of the disclosure. In FIG. 19 , the nodescorresponding to non-empty space are colored in grey, and referred to asshaded nodes. The nodes corresponding to empty space are colored inwhite, and can be referred to as blank nodes. The shaded nodes arenumbered according to a coding sequence.

In the FIG. 19 example, the nodes at depth 1 are LCUs. For example, thetree structure (1900) includes a first LCU (1910) and a second LCU(1920). The point cloud can be coded using node-based geometry andattribute coding (also referred to as LCU based geometry and attributecoding). In some examples of node-based geometry and attribute coding inthe sub-volume based interleave manner at depth 1, the geometryinformation of nodes in the first LCU (1910) is coded and attributeinformation of the nodes in the first LCU (1910) is coded after thegeometry information of the nodes in the first LCU (1910). Then, thegeometry information of nodes in the second LCU (1920) is coded afterthe attribute information of the nodes in the first LCU (1910), andattribute information of the nodes in the second LCU (1920) is codedafter the geometry information of the nodes in the second LCU (1920).For simplicity, a node at depth k (k=1 in FIG. 19 ) can be referred toas an LCU in the present disclosure. In the present disclosure, LCU anda node at depth k can be interchangeable.

In some examples, node-based geometry and attribute coding is performedin the node based interleave manner. For example, the geometryinformation of node 1 is coded and then attribute information of thenode 1 is coded after the geometry information of the node 1. Then, thegeometry information of node 2 is coded after the attribute informationof the node 1, and attribute information of the node 2 is coded afterthe geometry information of the node 2.

For simplicity, quadtree partitions are used in the tree structure(1900) for illustration. It is noted that other partitions, such asoctree partitions, binary tree partitions can be used in a treestructure.

According to an aspect of the disclosure, on the encoder side, thegenerated bitstreams for both geometry and attributes of each node(e.g., LCU) can be transmitted without waiting for the completion of thegeometry coding of the whole point cloud. On the decoder side, a decodercan decode the points in a corresponding node (e.g., LCU) and displaythem without waiting for the completion of the decoding of the geometryof the whole point cloud. Thus, low latency encoding and decoding can beachieved by using node based geometry and attribute coding.

It is noted that occupied nodes at depth k (e.g., LCUs) can be coded inany suitable order. In an example, the occupied nodes at depth k (e.g.,LCUs) are coded in Morton order. In another example, the occupied nodesat depth k are coded in other space-filling order, other than Mortoncode.

In some examples, coding of geometry and attribute of an LCU doesn'tdepend on information of its neighboring LCU(s). Specifically,predictions/references across LCU boundaries are disabled, and thecontext and history information are reinitialized for each LCU as well.The history information can include geometry occupancy information ofnodes with depth smaller than k. Thus, LCUs can be encoded or decodedindependently (referred to as LCU coding independence). The LCU codingindependence can enable maximum parallelism at depth k, i.e., LCU levelparallel encoding and decoding.

In some other examples, coding of geometry and attribute of an LCU canrely on information of its neighbors (e.g., neighboring LCUs). Forexample, coding of geometry and attribute of an LCU relies oninformation of its already coded neighboring nodes and their coded childnodes (referred to as LCU coding dependence). The LCU coding dependencecan help to gain better compression efficiency.

In some embodiments, parallel node based (LCU based) coding techniquescan be used in point cloud coding. To achieve parallel node based(LCU-based) coding, coding of geometry and attribute of an LCU cannotdepend on information of its neighboring LCUs. To achieve parallelprocessing and avoid uncertainty, predictions/references across LCUboundaries are disabled and coding state information, such as thecontext information and history information need to be reinitialized toa known coding state consistent at the encoder and the decoder side, foreach LCU as well.

In an embodiment, at each LCU, the coding state, such as the context forentropy coding, geometry occupancy history information, and othernecessary state information of LCU based coding, can be set to aninitial state, which is the coding state when coding of the point cloudstarts.

In another embodiment, instead of using the initial state, a suitableintermediate coding state, such as a coding state (including context forentropy coding and geometry occupancy history information, and othernecessary state information of LCU based coding), right before the firstLCU is reached (e.g., after the coding of tree depth (k−1) is completed)can be stored. The nodes at tree depth k can be coded according to LCUbased coding. When encoding each LCU, the coding state can be set basedon the stored intermediate coding state. Then, parallel node-based(LCU-base) coding can be achieved. In addition, the stored intermediatecoding state may help improve the coding performance, compared to theinitial coding state when the coding process starts.

FIG. 20 shows a flow chart outlining a coding process (2000) for codinggeometry information and/or attribute information according to someembodiments of the disclosure.

At (S2010), coding of depth (k−1) of a tree structure is completedbefore the coding of any occupied nodes at depth k. The occupied nodesat depth k of the tree structure are referred as LCU nodes, and the LCUnodes respectively correspond to subtrees in the tree structure, and canbe coded by LCU based coding (e.g., LCU based geometry coding, LCU basedgeometry and attribute coding). In some examples, the LCU nodes can becoded in parallel.

At (S2020), a variable N is set to be the number of LCUs at depth k(e.g., occupied nodes at the depth k) of the tree structure and avariable I is initialized, for example to 0. The variable I is used totrack the index of the LCUs.

At (S2030), the variable I is compared with the variable N. When thevariable I is smaller than variable N, the process proceeds to (S2040);otherwise, coding of the LCUs are completed, and the process can proceedto further coding process.

At (S2040), when the variable I is equal to 0, the corresponding LCUwith the index having the value of the variable I is the first LCU, thenthe process proceeds to (S2050); otherwise, the process proceeds to(S2060).

At (S2050), the coding state (including context for entropy coding andgeometry occupancy history information, and other necessary stateinformation of LCU based coding) is stored.

At (S2060), the coding state is restored from the stored coding state.

At (S2070), the LCU with index having the value of the variable I iscoded using LCU based coding.

At (S2080), the variable I increases by one, and the process returns to(S2030).

It is noted that the process (2000) can be suitably modified.

FIG. 21 shows a flow chart outlining another coding process (2100) forcoding geometry information and/or attribute information according tosome embodiments of the disclosure.

At (S2110), coding of depth (k−1) of a tree structure is completedbefore the coding of occupied nodes at depth k. The occupied nodes atdepth k of the tree structure are referred as LCU nodes, and the LCUnodes respectively correspond to subtrees in the tree structure, and canbe coded by LCU based coding (e.g., LCU based geometry coding, LCU basedgeometry and attribute coding). In some examples, the LCU nodes can becoded in parallel.

At (S2120), a variable N is set to be the number of LCUs at depth k(e.g., number of occupied nodes at depth k) of the tree structure and avariable I is initialized, for example to 0. The variable I is used totrack the index of the LCUs.

At (S2130), the coding state (including context for entropy coding andgeometry occupancy history information, and other necessary stateinformation of LCU based coding) is stored.

At (S2140), the variable I is compared with the variable N. When thevariable I is smaller than variable N, the process proceeds to (S2150);otherwise, coding of the LCUs are completed, and the process can proceedto further coding process.

At (S2150), the coding state is restored from the stored coding state.

At (S2160), the LCU with index having the value of the variable I iscoded using LCU based geometry and attribute coding.

At (S2170), the variable I increases by one, and the process returns to(S2140).

According to some aspects of the disclosure, a flag is used to provideadditional information to control the storage and restore of the codingstate information for the LCU based coding. The techniques disclosed inthe present disclosure can be applied in any suitable software, standardor system for PCC.

In the implementation of parallel node-based (LCU-based) coding, thecoding state can be saved right before the first LCU is reached. Whencoding each LCU, the saved coding state can be restored. Thus, thuscoding state for LCUs can be known and consistent at the encoder sideand decoder side. In this way, parallel node-based (LCU-base) coding canbe achieved. In some examples, parallel coding is not needed, for thepurpose of reducing memory and operations, the coding state informationdoes not have to be saved and restored.

According to an aspect of the disclosure, a flag can be used to controlwhether to save and restore the coding state. In some examples, the flagcan be added in the high-level syntax, for example, in the sequenceparameter set, geometry parameter set or slice header to control whetherto save and restore the coding state. The flag can be referred to asstate store flag.

FIG. 22A shows a syntax table in a geometry header (2200A) according tosome embodiments of the disclosure. The geometry header (2200A) includesa geometry parameter set that can be transmitted in the coded bitstreamthat carries the point cloud from the encoder side to the decoder side.The geometry parameter set includes a flag (2210), for example with aname “gps_save_state_flag”, to control whether to save and restore thecoding state. Specifically, in some examples, in response to the flag“gps_save_state_flag” having a binary value “1”, coding stateinformation before the first LCU is stored, and before coding each LCU(or each subsequent LCU), coding state can be restored according to thestored coding state information. On the other hand, in response to theflag “gps_save_state_flag” having a binary value “0”, no coding statestoring and restoring operations are performed.

In some examples, LCU coding only includes geometry coding, the statestore flag is included in the geometry header, such as the geometryheader (2200A).

In some examples, LCU coding includes both geometry coding and attributecoding, and the state store flag is included in sequence parameter setto control the LCU coding of both geometry and attribute coding.

FIG. 22B shows a syntax table in a sequence header (2200B) according tosome embodiments of the disclosure. The sequence header (2200B) includesa sequence parameter set that can be transmitted in the coded bitstreamthat carries the point cloud from the encoder side to the decoder side.The sequence parameter set includes a flag (2220), for example with aname “save_state_flag”, to control whether to save and restore thecoding state. Specifically, in some examples, in response to the flag“save_state_flag” having a binary value “1”, coding state informationbefore the first LCU is stored, and before coding each LCU (or eachsubsequent LCU), coding state can be restored according to the storedcoding state information. On the other hand, in response to the flag“save_state_flag” having a binary value “0”, no coding state storing andrestoring operations are performed.

FIG. 23 shows a flow chart outlining a coding process (2300) for codinggeometry information and/or attribute information according to someembodiments of the disclosure.

At (S2310), coding of depth (k−1) of a tree structure is completedbefore the coding of any occupied nodes at depth k. The occupied nodesat depth k of the tree structure are referred as LCU nodes, and the LCUnodes respectively correspond to subtrees in the tree structure, and canbe coded by LCU based coding (e.g., LCU based geometry coding, LCU basedgeometry and attribute coding). In some examples, the LCUs can be codedin parallel. In some examples, the LCUs are coded one by one, not byparallel processing.

At (S2320), a variable N is set to be the number of LCUs at depth k(e.g., occupied nodes at the depth k) of the tree structure and avariable I is initialized, for example to 0. The variable I is used totrack the index of the LCUs.

At (S2330), the variable I is compared with the variable N. When thevariable I is smaller than variable N, the process proceeds to (S2340);otherwise, coding of the LCUs are completed, and the process can proceedto further coding process.

At (S2340), when the variable I is equal to 0, the corresponding LCUwith the index having the value of the variable I is the first LCU, thenthe process proceeds to (S2350); otherwise, the process proceeds to(S2360).

At (S2350), if a flag that is referred to as state store flag, such asthe flag “gps_save_state_flag” in the case of LCU based geometry coding,or the flag “save_state_flag” in the case of LCU based geometry andattribute coding, is equal to binary value “1”, the process proceeds to(S2355) for storing coding state; otherwise, the process proceeds to(S2370) without storing the coding state.

At (S2355), the coding state (including context for entropy coding andgeometry occupancy history information, and other necessary stateinformation of LCU based coding) is stored.

At (S2360), if the state store flag, such as the flag“gps_save_state_flag” in the case of LCU based geometry coding, or theflag “save_state_flag” in the case of LCU based geometry and attributecoding, is equal to binary value “1”, the process proceeds to (S2365)for restoring the coding state; otherwise, the process proceeds to(S2370) without restoring the coding state.

At (S2365) the coding state is restored from the stored coding state.

At (S2370), the LCU with index having the value of the variable I iscoded using LCU based coding.

At (S2380), the variable I increases by one, and the process returns to(S2330).

It is noted that, in some examples, when the state store flag (e.g., theflag “gps_save_state_flag” in the case of LCU based geometry coding, orthe flag “save_state_flag” in the case of LCU based geometry andattribute coding) has the binary value “1”, the LCUs are coded inparallel; and when the state store flag (e.g., the flag“gps_save_state_flag” in the case of LCU based geometry coding, or theflag “save_state_flag” in the case of LCU based geometry and attributecoding) has the binary value “0”, LCUs are coded one by one, not byparallel processing.

It is noted that the process (2300) can be suitably modified.

FIG. 24 shows a flow chart outlining another coding process (2400) forcoding geometry information and/or attribute information according tosome embodiments of the disclosure.

At (S2410), coding of depth (k−1) of a tree structure is completedbefore the coding of occupied nodes at depth k. The occupied nodes atdepth k of the tree structure are referred as LCU nodes, and the LCUnodes respectively correspond to subtrees in the tree structure, and canbe coded by LCU based coding (e.g., LCU based geometry coding, LCU basedgeometry and attribute coding). In some examples, the LCUs can be codedin parallel. In some examples, the LCUs are coded one by one, not byparallel processing.

At (S2420), a variable N is set to be the number of LCUs at depth k(e.g., number of occupied nodes at depth k) of the tree structure and avariable I is initialized, for example to 0. The variable I is used totrack the index of the LCUs.

At (S2430), the coding state (including context for entropy coding andgeometry occupancy history information, and other necessary stateinformation of LCU based coding) is stored. It is noted that in someexamples, the coding state is stored when a flag that is referred to asa state store flag (e.g., the flag “gps_save_state_flag” in the case ofLCU based geometry coding, or the flag “save_state_flag” in the case ofLCU based geometry and attribute coding) is equal to binary value “1”.

At (S2440), the variable I is compared with the variable N. When thevariable I is smaller than variable N, the process proceeds to (S2445);otherwise, coding of the LCUs are completed, and the process can proceedto further coding process.

At (S2445), if a flag that is referred to as a state store flag (e.g.,the flag “gps_save_state_flag” in the case of LCU based geometry coding,or the flag “save_state_flag” in the case of LCU based geometry andattribute coding) is equal to binary value “1”, the process proceeds to(S2450) for restoring the coding state; otherwise, the process proceedsto (S2460) without restoring the coding state.

At (S2450), the coding state is restored from the stored coding state.

At (S2460), the LCU with index having the value of the variable I iscoded using LCU based geometry coding or LCU based geometry andattribute coding.

At (S2470), the variable I increases by one, and the process returns to(S2440).

FIG. 25 shows a flow chart outlining a process (2500) according to anembodiment of the disclosure. The process (2500) can be used during adecoding process for a point cloud. In various embodiments, the process(2500) is executed by processing circuitry, such as the processingcircuitry in the terminal devices (110), the processing circuitry thatperforms functions of the encoder (203) and/or the decoder (210), theencoder (700), and/or the decoder (800), and the like. In someembodiments, the process (2500) is implemented in software instructions,thus when the processing circuitry executes the software instructions,the processing circuitry performs the process (2500). The process startsat (S2501) and proceeds to (S2510).

At (S2510), a flag that indicates an enable/disable control for savingcoding state in a largest coding unit (LCU) based coding of a pointcloud is determined. In some embodiments, the flag is decoded from atleast one of a sequence parameter set in a sequence header, a geometryparameter set in a geometry header, and a slice header.

At (S2520), coding state information before a coding of a first LCU isstored. In an example, the coding state information is stored in inresponse to the flag indicating an enable control. In another example,the storing of the coding state information is skipped in response tothe flag indicating the disable control for saving coding state. In anexample, the coding state information is stored regardless of the flag.

In some examples, the coding state information includes at least one ofcontext information, history geometry occupancy information and othersuitable history information.

At (S2530), in response to the flag indicating the enable control, acoding state before a coding of a second LCU is restored according tothe stored coding state information. In some examples, the restoring ofthe coding state according to the stored coding state information beforethe coding of the second LCU can be skipped in response to the flagindicating a disable control for saving coding state.

In some embodiments, the flag is decoded from a geometry parameter setin a geometry header, such as the flag “gps_save_state_flag”. Then, thecoding state information is stored before a geometry coding of the firstLCU; and the coding state is restored before a geometry coding of thesecond LCU according to the stored coding state information.

In some embodiments, the flag is decoded from a sequence parameter setin a sequence header, such as the flag “save_state_flag”. Then, thecoding state information is stored before a geometry and attributecoding of the first LCU; and the coding state is restored, before ageometry and attribute coding of the second LCU, according to the storedcoding state information.

In some embodiments, the LCUs can be coded in parallel. For example, thecoding of the second LCU can start without a completion of the coding ofthe first LCU.

Then, the process proceeds to (S2599) and terminates.

The techniques disclosed in the present disclosure may be usedseparately or combined in any order. Further, each of the techniques(e.g., methods, embodiments), encoder, and decoder may be implemented byprocessing circuitry (e.g., one or more processors or one or moreintegrated circuits). In some examples, the one or more processorsexecute a program that is stored in a non-transitory computer-readablemedium.

The techniques described above, can be implemented as computer softwareusing computer-readable instructions and physically stored in one ormore computer-readable media. For example, FIG. 26 shows a computersystem (2600) suitable for implementing certain embodiments of thedisclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by one or more computer central processingunits (CPUs), Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 26 for computer system (2600) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (2600).

Computer system (2600) may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (2601), mouse (2602), trackpad (2603), touchscreen (2610), data-glove (not shown), joystick (2605), microphone(2606), scanner (2607), camera (2608).

Computer system (2600) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (2610), data-glove (not shown), or joystick (2605), butthere can also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers (2609), headphones(not depicted)), visual output devices (such as screens (2610) toinclude CRT screens, LCD screens, plasma screens, OLED screens, eachwith or without touch-screen input capability, each with or withouttactile feedback capability—some of which may be capable to output twodimensional visual output or more than three dimensional output throughmeans such as stereographic output; virtual-reality glasses (notdepicted), holographic displays and smoke tanks (not depicted)), andprinters (not depicted).

Computer system (2600) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(2620) with CD/DVD or the like media (2621), thumb-drive (2622),removable hard drive or solid state drive (2623), legacy magnetic mediasuch as tape and floppy disc (not depicted), specialized ROM/ASIC/PLDbased devices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (2600) can also include an interface (2654) to one ormore communication networks (2655). Networks can for example bewireless, wireline, optical. Networks can further be local, wide-area,metropolitan, vehicular and industrial, real-time, delay-tolerant, andso on. Examples of networks include local area networks such asEthernet, wireless LANs, cellular networks to include GSM, 3G, 4G, 5G,LTE and the like, TV wireline or wireless wide area digital networks toinclude cable TV, satellite TV, and terrestrial broadcast TV, vehicularand industrial to include CANBus, and so forth. Certain networkscommonly require external network interface adapters that attached tocertain general purpose data ports or peripheral buses (2649) (such as,for example USB ports of the computer system (2600)); others arecommonly integrated into the core of the computer system (2600) byattachment to a system bus as described below (for example Ethernetinterface into a PC computer system or cellular network interface into asmartphone computer system). Using any of these networks, computersystem (2600) can communicate with other entities. Such communicationcan be uni-directional, receive only (for example, broadcast TV),uni-directional send-only (for example CANbus to certain CANbusdevices), or bi-directional, for example to other computer systems usinglocal or wide area digital networks. Certain protocols and protocolstacks can be used on each of those networks and network interfaces asdescribed above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (2640) of thecomputer system (2600).

The core (2640) can include one or more Central Processing Units (CPU)(2641), Graphics Processing Units (GPU) (2642), specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(2643), hardware accelerators for certain tasks (2644), graphicsadapters (2650), and so forth. These devices, along with Read-onlymemory (ROM) (2645), Random-access memory (2646), internal mass storagesuch as internal non-user accessible hard drives, SSDs, and the like(2647), may be connected through a system bus (2648). In some computersystems, the system bus (2648) can be accessible in the form of one ormore physical plugs to enable extensions by additional CPUs, GPU, andthe like. The peripheral devices can be attached either directly to thecore's system bus (2648), or through a peripheral bus (2649). In anexample, the screen (2610) can be connected to the graphics adapter(2650). Architectures for a peripheral bus include PCI, USB, and thelike.

CPUs (2641), GPUs (2642), FPGAs (2643), and accelerators (2644) canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(2645) or RAM (2646). Transitional data can be also be stored in RAM(2646), whereas permanent data can be stored for example, in theinternal mass storage (2647). Fast storage and retrieve to any of thememory devices can be enabled through the use of cache memory, that canbe closely associated with one or more CPU (2641), GPU (2642), massstorage (2647), ROM (2645), RAM (2646), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (2600), and specifically the core (2640) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (2640) that are of non-transitorynature, such as core-internal mass storage (2647) or ROM (2645). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (2640). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(2640) and specifically the processors therein (including CPU, GPU,FPGA, and the like) to execute particular processes or particular partsof particular processes described herein, including defining datastructures stored in RAM (2646) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator (2644)), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method for point cloud coding, comprising:determining, by a processor, a flag that indicates an enable/disablecontrol for saving coding state in a parallel largest coding unit (LCU)based coding of a point cloud; in response to completion of coding at adepth (k−1) of an octree partition of the point cloud, storing, by theprocessor, coding, state information after the completion of the codingof the depth (k−1) and before a coding at depth k of the octreepartition, k being an integer greater than or equal to 1; and inresponse to the flag indicating an enable control, coding a first LCU bythe processor, using a coding state restored according to the codingstate information stored after the completion of the coding of the depth(k−1) and before the coding at the depth k; and coding a second LCU, bythe processor, using the coding state restored according to the codingstate information stored after the completion of the coding of the depth(k−1) and before the coding at the depth k, the first LCU and the secondLCU being different LCUs located at the depth of the octree partition ofthe point cloud.
 2. The method of claim 1, further comprising: storing,in response to the flag indicating the enable control, the coding stateinformation before the coding at the depth k.
 3. The method of claim 2,further comprising: skipping, in response to the flag indicating adisable control, the storing of the coding state information.
 4. Themethod of claim 1, further comprising: in response to the flagindicating a disable control, coding the first LCU and the second LCU atthe depth k without using the coding state restored according to thecoding state information stored before the coding at the depth k.
 5. Themethod of claim 1, further comprising: decoding the flag from at leastone of a sequence parameter set in a sequence header, a geometryparameter set in a geometry header, or a slice header.
 6. The method ofclaim 1, further comprising: decoding the flag from a geometry parameterset in a geometry header; storing the coding state information before ageometry coding at the depth k; and restoring the coding state accordingto the stored coding state information before a geometry coding of thefirst LCU at the depth k.
 7. The method of claim 1, further comprising:decoding the flag from a sequence parameter set in a sequence header;storing the coding state information before a geometry and attributecoding at the depth k; and restoring the coding state according to thestored coding state information before a geometry and attribute codingof the first LCU at the depth k.
 8. The method of claim 1, furthercomprising: coding the second LCU without a completion of the coding ofthe first LCU.
 9. The method of claim 1, further comprising: coding thesecond LCU and the first LCU without using the coding state restoredaccording to the coding state information stored before the coding atthe depth k in response to the flag indicating a disable control. 10.The method of claim 9, further comprising: skipping the storing of thecoding state information in response to the flag indicating the disablecontrol.
 11. The method of claim 1, wherein the coding state informationincludes at least one of context information and history geometryoccupancy information.
 12. An apparatus for point cloud coding,comprising: processing circuitry configured to: determine a flag thatindicates an enable/disable control for saving coding state in aparallel largest coding unit (LCU) based coding of a point cloud; inresponse to completion of coding at a depth (k−1) of an octree partitionof the point cloud, store coding state information after the completionof the coding of the depth (k−1) and before a coding at depth k of theoctree partition, k being an integer greater than or equal to 1; and inresponse to the flag indicating an enable control, coding a first LCU atthe depth k using a coding state restored according to the coding stateinformation stored after the completion of the coding of the depth (k−1)and before the coding at the depth k; and coding a second LCU using thecoding state restored according to the coding state information storedafter the completion of the coding of the depth (k−1) and before thecoding at the depth k, the first LCU and the second LCU being differentLCUs located at the depth k of the octree partition of the point cloud.13. The apparatus of claim 12, wherein the processing circuitry isconfigured to: store, in response to the flag indicating the enablecontrol, the coding state information before the coding at the depth k.14. The apparatus of claim 13, wherein the processing circuitry isconfigured to: skip, in response to the flag indicating a disablecontrol, the storing of the coding state information.
 15. The apparatusof claim 12, wherein the processing circuitry is configured to: inresponse to the flag indicating a disable control, code the first LCUand the second LCU at the depth k without using the coding staterestored according to the coding state information stored before thecoding at the depth k.
 16. The apparatus of claim 12, wherein theprocessing circuitry is configured to: decode the flag from at least oneof a sequence parameter set in a sequence header, a geometry parameterset in a geometry header, or a slice header.
 17. The apparatus of claim12, wherein the processing circuitry is configured to: decode the flagfrom a geometry parameter set in a geometry header; store the codingstate information before a geometry coding at the depth k; and restorethe coding state according to the stored coding state information beforea geometry coding of the first LCU at the depth k.
 18. The apparatus ofclaim 12, wherein the processing circuitry is configured to: decode theflag from a sequence parameter set in a sequence header; store, thecoding state information before a geometry and attribute coding at thedepth k; and restore, the coding state according to the stored codingstate information before a geometry and attribute coding of the firstLCU at the depth k.
 19. The apparatus of claim 12, wherein theprocessing circuitry is configured to: code the second LCU without acompletion of the coding of the first LCU.
 20. The apparatus of claim12, wherein the processing circuitry is configured to: code the secondLCU and the first LCU without using the coding state restored accordingto the coding state information stored before the coding at the depth kin response to the flag indicating a disable control.